Abstract Manual methods of Leaf area estimation are very time consuming and tiresome processes and these are generally inaccurate and subjective. A new approach is presented in this paper to make this process more accurate and user friendly by using Digital Image Processing techniques on leaves of healthy and diseased plants. A coin of five rupees is taken as a reference object to estimate... Manual methods of Leaf area estimation are very time consuming and tiresome processes and these are generally inaccurate and subjective. A new approach is presented in this paper to make this process more accurate and user friendly by using Digital Image Processing techniques on leaves of healthy and diseased plants. A coin of five rupees is taken as a reference object to estimate the leaf area. Coins have been used in previous approaches in literature but none of the proposed methods have segmented coins accurately. Our method proposes a new approach for coin segmentation by using Hough transform. The main advantage of our proposed method is the area estimation of not only healthy leaves but also the diseased ones. Our method has been tested on four kinds of plants i.e. Potato, Pigeon Pea, Green Gram and Black Gram. Twenty leaves have been taken for each of these four plants in which ten leaves were healthy and remaining ten were of the diseased plants. The results obtained from this method were compared to the graph paper method. The average error rate of leaf area estimation of our proposed method are 1.49%, 1.11% , 1.71% and 1.31% for potato, Pigeon Pea, Green Gram and Black Gram respectively as compared to the results of graph paper method. It proves that our proposed method is more accurate for leaf area estimation of healthy and diseased leaves. Read More Read Less

Abstract Feature extraction is the key and foundation of content-based retrieval of video and image. In order to realize the content-based index and retrieval of binocular stereo vision resources efficiently, the method of feature extraction based on Principal Component Analysis-Histogram of Oriented Depth Gradient (PCA-HODG) and Main Color Histograms (MCH) is proposed. In the method, on the one hand, for the depth map obtained... Feature extraction is the key and foundation of content-based retrieval of video and image. In order to realize the content-based index and retrieval of binocular stereo vision resources efficiently, the method of feature extraction based on Principal Component Analysis-Histogram of Oriented Depth Gradient (PCA-HODG) and Main Color Histograms (MCH) is proposed. In the method, on the one hand, for the depth map obtained from matching of right image and left image, the PCAHODG algorithm is proposed to extract shape features. In the algorithm, edge detection and gradient calculation in depth map windows are performed to obtain the regional shape histogram features. Moreover, sliding window detection over a depth map is performed to extract the full features. At the same time, in feature extraction of depth map windows and full depth map, principal component analysis is used to realize dimensional reduction respectively. On the other hand, for the left image of binocular stereo vision, the improved MCH algorithm is used to extract color features. Then the shape and color descriptors can be obtained as 2-dimensional factors for similarity calculation. The experimental results show that the proposed method can detect and extract the features of binocular stereo vision image more effectively and achieve similar classification more accurately compared with the existing HOD, RSDF and GIF algorithms. Moreover, the proposed method also has better robustness. Read More Read Less

Abstract Significant research works have been devoted to asymmetric stereoscopic coding technique. Based on the perceptual theory of binocular suppression, in this method, one view of the stereo pair can be coded with a lower quality than the other, thus allowing to decrease bandwidth requirement for 3D visual content delivery without degrading subjective quality. However, with the condition that the quality gap between both... Significant research works have been devoted to asymmetric stereoscopic coding technique. Based on the perceptual theory of binocular suppression, in this method, one view of the stereo pair can be coded with a lower quality than the other, thus allowing to decrease bandwidth requirement for 3D visual content delivery without degrading subjective quality. However, with the condition that the quality gap between both images does not exceed a certain threshold. Some works proposed to fix this threshold by subjective experiments. Nevertheless, using a fixed threshold does not allow adaptation to the stereo image content and presents dependency to the experimental design. In this paper, we propose a novel method that selects automatically the maximum tolerable quality gap between views for different stereo image pairs. This has been achieved by modeling the relationship between the asymmetric quality and the interview distortion between corresponding pixels. Then, the derived relationship is combined with Binocular Just Noticeable Difference (BJND) model to control the quality threshold between the left and right views. Experimental results showed that the proposed coding method can achieve considerable bitrate saving up to 16.32% and outperforms the widely used asymmetric coding approaches. Read More Read Less